# -*- coding: utf-8 -*-

''' Autor: Neuza Figueira - n.º 6036 '''

from bubblesort import BubbleSort
from random import uniform
from time import clock
import matplotlib.pyplot as plt

class RadixSort():
        ''' Class to handle Radix sort implementation and testing.'''
        
        def radix_sort(self, A):
                '''Python Implementation of Radix sort.'''
                '''@param A -> List to sort.'''
                bubble = BubbleSort()
                n = len(A)
                for i in range(n - 1, 0, -1):
                        A = bubble.bubblesort(A)
                        pass
                        return A
                pass



        def testRadixSort(self):
                '''Testing the complexity of Radix sort.'''
                Z = range(50, 1050, 50)
                T = []
                M = 50
                for n in Z:
                    A = [ uniform(0.0, 1.0) for k in xrange(n)]
                    tempos = []
                    for k in range(M):
                        t1 = clock()
                        self.radix_sort(A)
                        t2 = clock()
                        tempos.append(t2-t1)
                    media = reduce(lambda x, y: x + y, tempos) / len(tempos)
                    var = reduce(lambda x, y: x + (y-media)**2, [0] + tempos) / len(tempos)
                    
                    T.append((n,media, var))

                X = [n for n, media, var in T]
                Y = [media for n, media, var in T]
                ct = 14e6
                Z = [(n**2) / ct for n, media, var in T]


                plt.grid(True)
                plt.ylabel(u'T(n) - tempo de execução médio em segundos')
                plt.xlabel(u'n - número de elementos')
                plt.plot(X,Y,'rs', label="resultado experimental")
                plt.plot(X, Z, 'b^', label=u"previsão teórica")
                plt.legend()
                plt.show()
                pass
